ANSR is hiring for one of its clients.
About ANSR MedTech:
Who We Are:
ANSR MedTech Capability Center is a new global innovation hub being established in India for a Fortune 100 Fastest-Growing Company in the MedTech sector. Built in partnership with ANSR, the center draws on ANSR's proven experience in establishing and scaling high-performance Global Capability Centers (GCCs) for leading global enterprises.
ANSR MedTech center brings together world-class engineering, product, and technology talent to build next-generation healthcare platforms and solutions that power global operations.
Data / Solution Engineer
Global Insights & Analytics CoE
Location: India GCC
Function: Growth Strategy & Insights / Global Insights & Analytics
Role Purpose:
The Data / Solution Engineer will build reusable analytical datasets, transformations, metric logic, derived measures, and product-ready data assets that power business-facing analytics products.
This role will partner closely with Product Owners, Solution Architects, Visualization / UX Engineers, QA / Operational Analysts, US I&A, and platform / data teams to convert business requirements into scalable, validated, and supportable analytical assets.
This is a hands-on builder role for a growing analytics capability. The ideal candidate may come from a data engineering, analytics engineering, BI engineering, or solution engineering background. The role requires strong ability to work with fragmented data sources, create reusable analytical logic, support analytics product delivery, and help reduce manual reporting and one-off analysis.
Key Responsibilities:
Analytical Data Build:
- Build analytical datasets, transformations, joins, metric logic, and reusable data assets to support analytics products.
- Connect and prepare data from commercial, CRM, patient journey, provider, payer/access, product, digital, support, and third-party sources.
- Translate approved solution designs into scalable analytical logic and fit-for-use data structures.
Metric Logic and Derived Attributes:
- Implement metric calculations, derived measures, synthetic attributes, flags, segments, status logic, and business rules.
- Support complex analytics logic such as patient journey status, access friction flags, risk indicators, provider opportunity flags, and next-action signals.
- Ensure logic is reusable across dashboards, reports, portals, models, and insight products.
Exploratory Analytics Support:
- Support Insights Analysts and Squad Leads by creating fast analytical views, data cuts, and evidence packs for exploratory business questions.
- Help identify patterns, gaps, anomalies, and data readiness issues during analysis.
- Partner with Product Owners and Solution Architects when exploratory work should become a reusable product or supported analytical asset.
Quality, Validation, and Documentation:
- Partner with QA / Operational Analysts to validate outputs, reconcile metrics, test edge cases, and confirm data accuracy.
- Document logic, assumptions, data sources, transformations, dependencies, and known limitations.
- Ensure analytical assets are supportable and understandable by downstream users and delivery teams.
Automation and Reuse:
- Reduce manual reporting and repeat data preparation through reusable logic, automated transformations, and standardized build patterns.
- Partner with Solution Architects to implement design standards and scalable patterns.
- Surface data, access, reliability, or platform constraints through the appropriate delivery and governance processes.
Qualifications:
- 5–8+ years of experience in analytics engineering, data engineering, BI engineering, data products, commercial analytics, or related fields.
- Strong experience with SQL and modern data platforms; Python / PySpark experience preferred.
- Experience building analytical datasets, transformations, metric logic, reusable data assets, or data products.
- Familiarity with Databricks, cloud data platforms, Delta / lakehouse environments, Tableau / Power BI, or similar tools is preferred.
- Strong understanding of data quality, reconciliation, validation, and documentation practices.
- Ability to work with ambiguous business requirements and translate them into usable analytical assets.
- Experience partnering with Product Owners, Solution Architects, visualization teams, QA, and business stakeholders.
- Experience in healthcare, MedTech, life sciences, commercial analytics, claims, payer/access, provider, patient journey, or product usage data is preferred.
- Experience working in a GCC / CoE or global delivery model is preferred.
